R.A.G. Development
What is RAG?
RAG (or Retrieval-Augmented Generation), is an AI framework that improves large language models (LLMs) by combining them with external, up-to-date data sources. Instead of relying only on their static training data, RAG models first retrieve relevant information from a knowledge base and then use that information to generate a more accurate, contextual, and current response.
Indexing Stage
We begin by preparing your external knowledge base for efficient retrieval, which includes collecting data from documents, databases, or APIs; cleaning and chunking text into meaningful segments; and generating vector embeddings that capture semantic meaning.
Retrieval Stage
When a user query is received, we preprocess and convert it into an embedding using the same model from indexing. The system performs similarity searches across the vector database to find the most relevant chunks using hybrid or re-ranking methods.
Fusion Stage
The retrieved data snippets are intelligently merged with the user’s original query to create a rich, context-aware prompt. This augmented input is then passed to the Large Language Model (LLM), enabling it to generate more precise and factually consistent responses.
Generation Stage
We validate and refine model outputs to ensure they remain accurate and traceable to your source data. This includes optional response verification, output ranking, and feedback-based fine-tuning for continuous improvement and reliability of the RAG system.